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Simulation-Based Evaluation of Methods, Data Types, and Temporal Sampling Schemes for Detecting Recent Population

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Genomic methods accurately detect recent population declines. Serial or two-time point sampling best reconstructs population size changes, while contemporary sampling reliably estimates current size and change with large datasets.

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Area of Science:

  • Population Genetics
  • Conservation Genomics
  • Bioinformatics

Background:

  • Understanding recent population trends is crucial for assessing species vulnerability and guiding conservation efforts.
  • Genomic data offers powerful tools for inferring past demographic events, but accuracy for recent declines (<120 generations) requires careful evaluation.

Purpose of the Study:

  • To assess the accuracy of various genomic methods for detecting recent population declines.
  • To compare the performance of different sampling strategies and data types in demographic inference.

Main Methods:

  • Simulated genomic data using forward-time (SLiM) and coalescent (msprime) simulations under diverse demographic scenarios.
  • Evaluated site frequency spectrum (SFS)-based (momi2, Stairway Plot) and linkage disequilibrium (LD)-based (NeEstimator, GONE) methods.
  • Assessed RAD-like and whole-genome sequencing data with contemporary-only, two-time point, and serial sampling schemes.

Main Results:

  • GONE and momi2 demonstrated high power (>80%) for detecting severe declines with sufficient sample sizes.
  • Two-sample and serial sampling accurately reconstructed population size changes.
  • Serial sampling proved robust to genotyping errors and minor allele frequency distortions.

Conclusions:

  • Contemporary-only sampling can reliably estimate current population size and recent changes, especially with large sample sizes or whole-genome data.
  • Specific methods (GONE, momi2) and sampling strategies (serial, two-time point) enhance the accuracy of demographic inference for recent population trends.
  • Findings guide the design of genomics studies for effective population monitoring and conservation.